With the decreasing cost of optical sensors and increasing computing power of microprocessors, vision-based systems have been widely accepted as an integral part of the feasible solutions to driver assistance. The ability of detecting other vehicles on the road is essential to sensing and interpreting driving environments, which enables important functions like adaptive cruise control and pre-crash sensing. Vehicle detection requires effective vision algorithms that can distinguish vehicles from complex road scenes accurately. A great challenge comes from the large variety of vehicle appearance as well as different scenarios of driving environments. Vehicles vary in size, shape and appearance, which lead to considerable amount of variance in the class of vehicle images. Illumination changes in outdoor environments introduce additional variation in vehicle appearance. Meanwhile, unpredictable traffic situations create a wide range of non-stationary backgrounds with complex clutters. Moreover, high degrees of reliability and fast processing are required for driver assistance tasks, which also increase the difficulty of the task.
Known vision techniques have been used in vehicle detection. A number of approaches use empirical knowledge about vehicle appearance, such as symmetry, horizontal and vertical occluding edges around vehicle boundaries to detect the rear-view appearance of vehicles. These methods are computationally efficient but lack robustness because the parameters (e.g., thresholds) involved in edge detection and hypothesis generation are sensitive to lighting conditions and the dynamic range in image acquisition. To achieve reliable vehicle detection, several appearance-based methods exploit machine learning and pattern classification techniques to obtain elaborated classifiers that separate the vehicle class from other image patterns. Bayesian classifiers have also been used for classification in which a mixture of Gaussian filters and histograms were used to model the class distribution of vehicles and non-vehicles. Another method uses neural network classifiers that are trained on image features obtained from local orientation coding. Still other methods use Support Vector Machines (SVMs) that are trained on wavelet features.
Many of the methods mentioned above use partial knowledge for vehicle detection. For example, appearance-based methods mainly utilize the knowledge about vehicle and non-vehicle appearance, while motion-based detectors focus on the knowledge about relative vehicle motion. To make a detection system reliable, all the available knowledge should be utilized in a principled manner. There is a need for a vehicle detection system which is capable of fusing multiple sources of data over multiple image frames in order to more consistently and more accurately detect a vehicle.